README.md

README.md

@@ -3,9 +3,9 @@ viewer: false
 tags: [uv-script, classification, vllm, structured-outputs, gpu-required]
 ---
 
-# Dataset Classification with vLLM
+# Dataset Classification Script
 
-Efficient text classification for Hugging Face datasets using vLLM with structured outputs. This script provides GPU-accelerated classification with guaranteed valid outputs through guided decoding.
+GPU-accelerated text classification for Hugging Face datasets with guaranteed valid outputs through structured generation.
 
 ## 🚀 Quick Start
 
@@ -22,21 +22,25 @@ That's it! No installation, no setup - just `uv run`.
 
 ## 📋 Requirements
 
-- **GPU Required**: This script uses vLLM for efficient inference
+- **GPU Required**: Uses GPU-accelerated inference
 - Python 3.10+
 - UV (will handle all dependencies automatically)
-- vLLM >= 0.6.6 (for guided decoding support)
+- vLLM >= 0.6.6
 
 ## 🎯 Features
 
-- **Guaranteed valid outputs** using vLLM's guided decoding with outlines
-- **Zero-shot classification** with structured generation
-- **GPU-optimized** with vLLM's automatic batching for maximum efficiency
-- **Default model**: HuggingFaceTB/SmolLM3-3B (fast 3B model, easily changeable)
+- **Guaranteed valid outputs** using structured generation with guided decoding
+- **Zero-shot classification** without training data required
+- **GPU-optimized** for maximum throughput and efficiency
+- **Default model**: HuggingFaceTB/SmolLM3-3B (fast 3B model with thinking capabilities)
 - **Robust text handling** with preprocessing and validation
-- **Three prompt styles** for different use cases
 - **Automatic progress tracking** and detailed statistics
 - **Direct Hub integration** - read and write datasets seamlessly
+- **Label descriptions** support for providing context to improve accuracy
+- **Reasoning mode** for interpretable classifications with thinking traces
+- **JSON output parsing** for reliable extraction from reasoning mode
+- **Optimized batching** with vLLM's automatic batch processing
+- **Multiple guided backends** - supports outlines, xgrammar, and more
 
 ## 💻 Usage
 
@@ -62,20 +66,50 @@ uv run classify-dataset.py \
 **Optional:**
 
 - `--model`: Model to use (default: **`HuggingFaceTB/SmolLM3-3B`** - a fast 3B parameter model)
-- `--prompt-style`: Choose from `simple`, `detailed`, or `reasoning` (default: `simple`)
+- `--label-descriptions`: Provide descriptions for each label to improve classification accuracy
+- `--enable-reasoning`: Enable reasoning mode with thinking traces (adds reasoning column)
 - `--split`: Dataset split to process (default: `train`)
 - `--max-samples`: Limit samples for testing
+- `--shuffle`: Shuffle dataset before selecting samples (useful for random sampling)
+- `--shuffle-seed`: Random seed for shuffling (default: 42)
 - `--temperature`: Generation temperature (default: 0.1)
 - `--guided-backend`: Backend for guided decoding (default: `outlines`)
 - `--hf-token`: Hugging Face token (or use `HF_TOKEN` env var)
 
-### Prompt Styles
+### Label Descriptions
 
-- **simple**: Direct classification prompt
-- **detailed**: Emphasizes exact category matching
-- **reasoning**: Includes brief analysis before classification
+Provide context for your labels to improve classification accuracy:
 
-All styles benefit from structured output guarantees - the model can only output valid labels!
+```bash
+uv run classify-dataset.py \
+  --input-dataset user/support-tickets \
+  --column content \
+  --labels "bug,feature,question,other" \
+  --label-descriptions "bug:something is broken,feature:request for new functionality,question:asking for help,other:anything else" \
+  --output-dataset user/tickets-classified
+```
+
+The model uses these descriptions to better understand what each label represents, leading to more accurate classifications.
+
+### Reasoning Mode
+
+Enable thinking traces for interpretable classifications:
+
+```bash
+uv run classify-dataset.py \
+  --input-dataset stanfordnlp/imdb \
+  --column text \
+  --labels "positive,negative,neutral" \
+  --enable-reasoning \
+  --output-dataset user/imdb-with-reasoning
+```
+
+When `--enable-reasoning` is used:
+- The model generates step-by-step reasoning using SmolLM3's thinking capabilities
+- Output includes three columns: `classification`, `reasoning`, and `parsing_success`
+- Final answer must be in JSON format: `{"label": "chosen_label"}`
+- Useful for understanding complex classification decisions
+- Trade-off: Slower but more interpretable
 
 ## 📊 Examples
 
@@ -96,8 +130,8 @@ uv run classify-dataset.py \
   --input-dataset user/support-tickets \
   --column content \
   --labels "bug,feature_request,question,other" \
-  --output-dataset user/tickets-classified \
-  --prompt-style reasoning
+  --label-descriptions "bug:code or product not working as expected,feature_request:asking for new functionality,question:seeking help or clarification,other:general comments or feedback" \
+  --output-dataset user/tickets-classified
 ```
 
 ### News Categorization
@@ -111,11 +145,54 @@ uv run classify-dataset.py \
   --model meta-llama/Llama-3.2-3B-Instruct
 ```
 
-## 🚀 Running on HF Jobs
+### Complex Classification with Reasoning
+
+```bash
+uv run classify-dataset.py \
+  --input-dataset user/customer-feedback \
+  --column text \
+  --labels "very_positive,positive,neutral,negative,very_negative" \
+  --label-descriptions "very_positive:extremely satisfied,positive:generally satisfied,neutral:mixed feelings,negative:dissatisfied,very_negative:extremely dissatisfied" \
+  --enable-reasoning \
+  --output-dataset user/feedback-analyzed
+```
 
-This script is optimized for [Hugging Face Jobs](https://huggingface.co/docs/hub/spaces-gpu-jobs) (requires Pro subscription or Team/Enterprise organization):
+This combines label descriptions with reasoning mode for maximum interpretability.
 
-````bash
+### ArXiv ML Research Classification
+
+Classify academic papers into machine learning research areas:
+
+```bash
+# Fast classification with random sampling
+uv run classify-dataset.py \
+  --input-dataset librarian-bots/arxiv-metadata-snapshot \
+  --column abstract \
+  --labels "llm,computer_vision,reinforcement_learning,optimization,theory,other" \
+  --label-descriptions "llm:language models and NLP,computer_vision:image and video processing,reinforcement_learning:RL and decision making,optimization:training and efficiency,theory:theoretical ML foundations,other:other ML topics" \
+  --output-dataset user/arxiv-ml-classified \
+  --split "train[:10000]" \
+  --max-samples 100 \
+  --shuffle
+
+# With reasoning for nuanced classification
+uv run classify-dataset.py \
+  --input-dataset librarian-bots/arxiv-metadata-snapshot \
+  --column abstract \
+  --labels "multimodal,agents,reasoning,safety,efficiency" \
+  --label-descriptions "multimodal:vision-language and cross-modal models,agents:autonomous agents and tool use,reasoning:reasoning and planning systems,safety:alignment and safety research,efficiency:model optimization and deployment" \
+  --enable-reasoning \
+  --output-dataset user/arxiv-frontier-research \
+  --split "train[:1000]" \
+  --max-samples 50
+```
+
+The reasoning mode is particularly valuable for academic abstracts where papers often span multiple topics and require careful analysis to determine the primary focus.
+
+## 🚀 Running on HF Jobs
+
+Optimized for [Hugging Face Jobs](https://huggingface.co/docs/hub/spaces-gpu-jobs) (requires Pro subscription or Team/Enterprise organization):
+```bash
 # Run on L4 GPU with vLLM image
 hf jobs uv run \
   --flavor l4x1 \
@@ -125,6 +202,7 @@ hf jobs uv run \
   --column text \
   --labels "positive,negative" \
   --output-dataset user/imdb-classified
+```
 
 ### GPU Flavors
 - `t4-small`: Budget option for smaller models
@@ -135,6 +213,27 @@ hf jobs uv run \
 
 ## 🔧 Advanced Usage
 
+### Random Sampling
+
+When working with ordered datasets, use `--shuffle` with `--max-samples` to get a representative sample:
+
+```bash
+# Get 50 random reviews instead of the first 50
+uv run classify-dataset.py \
+  --input-dataset stanfordnlp/imdb \
+  --column text \
+  --labels "positive,negative" \
+  --output-dataset user/imdb-sample \
+  --max-samples 50 \
+  --shuffle \
+  --shuffle-seed 123  # For reproducibility
+```
+
+This is especially important for:
+- Chronologically ordered datasets (news, papers, social media)
+- Pre-sorted datasets (by rating, category, etc.)
+- Testing on diverse samples before processing the full dataset
+
 ### Using Different Models
 
 By default, this script uses **HuggingFaceTB/SmolLM3-3B** - a fast, efficient 3B parameter model that's perfect for most classification tasks. You can easily use any other instruction-tuned model:
@@ -147,7 +246,7 @@ uv run classify-dataset.py \
   --labels "contract,patent,brief,memo,other" \
   --output-dataset user/legal-classified \
   --model Qwen/Qwen2.5-7B-Instruct
-````
+```
 
 ### Large Datasets
 
@@ -166,15 +265,16 @@ uv run classify-dataset.py \
 - **SmolLM3-3B (default)**: ~50-100 texts/second on A10
 - **7B models**: ~20-50 texts/second on A10
 - vLLM automatically optimizes batching for best throughput
+- Performance scales with GPU memory and compute capability
 
 ## 🤝 How It Works
 
-1. **vLLM**: Provides efficient GPU batch inference
-2. **Guided Decoding**: Uses outlines to guarantee valid label outputs
+1. **vLLM**: Provides efficient GPU batch inference with automatic batching
+2. **Guided Decoding**: Uses outlines backend to guarantee valid label outputs
 3. **Structured Generation**: Constrains model outputs to exact label choices
 4. **UV**: Handles all dependencies automatically
 
-The script loads your dataset, preprocesses texts, classifies each one using guided decoding to ensure only valid labels are generated, then saves the results as a new column in the output dataset.
+The script loads your dataset, preprocesses texts, classifies each one with guaranteed valid outputs, then saves the results as a new column in the output dataset.
 
 ## 🐛 Troubleshooting
 
@@ -212,43 +312,28 @@ If you see `ImportError: cannot import name 'GuidedDecodingParams'`:
 - The script specifies the correct version in its dependencies
 - UV should automatically install the correct version
 
-## 🔬 Advanced Example: ArXiv ML Trends Analysis
+## 🔬 Advanced Workflows
 
-For a more complex real-world example, we provide scripts to analyze ML research trends from ArXiv papers:
+For complex real-world workflows that integrate UV scripts with the Python HF Jobs API, see the [ArXiv ML Trends example](examples/arxiv-workflow/). This demonstrates:
 
-### Step 1: Prepare the Dataset
-
-```bash
-# Filter and prepare ArXiv CS papers from 2024
-uv run prepare_arxiv_2024.py
-```
+- **Multi-stage pipelines**: Data preparation → GPU classification → Analysis
+- **Python API orchestration**: Using `run_uv_job()` to manage GPU jobs programmatically
+- **Production patterns**: Error handling, parallel execution, and incremental updates
+- **Cost optimization**: Choosing appropriate compute resources for each task
 
-This creates a filtered dataset of CS papers with combined title+abstract text.
+```python
+# Example: Submit a classification job via Python API
+from huggingface_hub import run_uv_job
 
-### Step 2: Run Classification with Python API
-
-```bash
-# Use HF Jobs Python API to classify papers
-uv run run_arxiv_classification.py
+job = run_uv_job(
+    script="https://huggingface.co/datasets/uv-scripts/classification/raw/main/classify-dataset.py",
+    args=["--input-dataset", "my/dataset", "--labels", "A,B,C"],
+    flavor="l4x1",
+    image="vllm/vllm-openai:latest"
+)
+result = job.wait()
 ```
 
-This script demonstrates:
-
-- Using `run_uv_job()` from the Python API
-- Classifying into modern ML trends (reasoning, agents, multimodal, robotics, etc.)
-- Handling authentication and job monitoring
-
-The classification categories include:
-
-- `reasoning_systems`: Chain-of-thought, reasoning, problem solving
-- `agents_autonomous`: Agents, tool use, autonomous systems
-- `multimodal_models`: Vision-language, audio, multi-modal
-- `robotics_embodied`: Robotics, embodied AI, manipulation
-- `efficient_inference`: Quantization, distillation, edge deployment
-- `alignment_safety`: RLHF, alignment, safety, interpretability
-- `generative_models`: Diffusion, generation, synthesis
-- `foundational_other`: Other foundational ML/AI research
-
 ## 📝 License
 
 This script is provided as-is for use with the UV Scripts organization.